Initial processing

This document describes the analysis of TCR repertoires for the manuscript “Unique roles of coreceptor-bound LCK in helper and cytotoxic T cells” by Horkova et al. The analysis workflow included:

• sorting of CD4 and CD8 cells from lymph nodes and thymi of Lck^WT, Lck^CA and Lck^CA/KR mice,
• isolation of RNA using RNA Clean & Concentrator-5 kit
• preparation of TCR-enriched libraries using the NEBNext® Mouse Immune Sequencing Kit
• sequencing the libraries on Illumina Miseq, 300bp paired-end reads
• demultiplexing reads in Illumina BaseSpace
• processing fastq files using the recommended pRESTO workflow on Galaxy with default parameters
• aligning processed fastq files using MiXCR

Raw data are deposited in the Sequence Read Archive (PRJNA872031). Processed files can be downloaded download from Zenodo. The Zenodo archive contains the following files, which are needed to run this script:

• merged outputs from MiXCR merged_TCR_repertoires.csv
• metadata file metadata_Lck.csv
• count tables with raw counts of CDR3 TRA and TRB sequences in each sample count_table_tra.csv, "count_table_trb.csv"
• TRA and TRB repertoires prepared for processing with the Immunarch package immdata_tra.rds, immdata_trb.rds

Let’s read the required files:

#
merged_repertoires <- read.csv("merged_TCR_repertoires.csv")
md <- read.csv("metadata_Lck.csv")

Count tables

We will start with processing of the file merged_TCR_repertoires.csv. This file contains outputs of MiXCR software, i.e., all the assembled CDR3 clonotypes, their counts in each sample, nucleotide and amino-acid sequences, all V, D and J alignments with scores, together with some meta-data. the column num_id contains info about the sample number, which we will frequently use to attach meta-data to sample numbers.

Here, we summarize the variables in the file merged_TCR_repertoires.csv:

kable(ExpData(data=merged_repertoires, type=2)) %>%
  kable_styling(full_width = F, font_size = 11, 
                bootstrap_options = c("striped", "hover", "condensed", "responsive"))

First, we will generate summary count tables for TRA and TRB CDR3 sequences (these tables are provided as Supplementary table S1 in the manuscript). We will reorder the file so that there will be one column for each sample, and we will change the num_id of the samples to meaningful names.

count_table_tra <- merged_repertoires %>% 
  dplyr::filter(chain == "TRA") %>% 
  dplyr::select(aaSeqCDR3, num_id, cloneCount)

counts_tra <- count_table_tra %>% 
  pivot_wider(names_from = num_id, values_from = cloneCount, values_fill = 0, values_fn = sum)

md2_tra <- merged_repertoires %>% filter(chain == "TRA") %>% group_by(aaSeqCDR3) %>% slice_head(n = 1)

excel_count_table_tra <- counts_tra %>% left_join(md2_tra %>% select(allVHitsWithScore, allDHitsWithScore, allJHitsWithScore))

md_to_join <- md %>% mutate(new_name = paste(Cell_type, Organ, Mouse_strain, paste0("Exp0",Exp)))  %>% select(num_id, new_name) %>% arrange(num_id)

order <- match(as.numeric(colnames(counts_tra)[2:29]), md_to_join$num_id)

colnames(excel_count_table_tra)[2:29] <- pull(md_to_join, new_name)[order]
excel_count_table_tra2 <- excel_count_table_tra %>% dplyr::select(1, 31, 32, 30, 22, 12, 25, 11, 6, 26, 13, 18, 4, 28, 17, 14, 8, 23, 9, 7, 16, 24, 2, 21, 5, 29, 10, 20, 19, 3, 15, 27)

Here, we show the resulting table for TRA:

kable(excel_count_table_tra2 %>% head) %>%
  kable_styling(full_width = F, font_size = 11, 
                bootstrap_options = c("striped", "hover", "condensed", "responsive"))

count_table_trb <- merged_repertoires %>% 
  dplyr::filter(chain == "TRB") %>% 
  dplyr::select(aaSeqCDR3, num_id, cloneCount)

counts_trb <- count_table_trb %>% 
  pivot_wider(names_from = num_id, values_from = cloneCount, values_fill = 0, values_fn = sum)

md2_trb <- merged_repertoires %>% 
  filter(chain == "TRB") %>% 
  group_by(aaSeqCDR3) %>% 
  slice_head(n = 1)

excel_count_table_trb <- counts_trb %>% 
  left_join(md2_trb %>% 
              select(allVHitsWithScore, allDHitsWithScore, allJHitsWithScore))

md_to_join <- md %>% 
  mutate(new_name = paste(Cell_type, Organ, Mouse_strain, paste0("Exp0",Exp))) %>% 
  select(num_id, new_name) %>% 
  arrange(num_id)

order <- match(as.numeric(colnames(counts_trb)[2:29]), md_to_join$num_id)

colnames(excel_count_table_trb)[2:29] <- pull(md_to_join, new_name)[order]

excel_count_table_trb2 <- excel_count_table_trb %>% 
  dplyr::select(1, 30:32, 22, 12, 25, 11, 6, 26, 13, 18, 4, 28, 17, 14, 8, 23, 9, 7, 16, 24, 2, 21, 5, 29, 10, 20, 19, 3, 15, 27)

Here, we show the resulting table for TRB:

kable(excel_count_table_trb2 %>% head) %>%
  kable_styling(full_width = F, font_size = 11, 
                bootstrap_options = c("striped", "hover", "condensed", "responsive"))

Prop tables and less than 5

Prop table TRA

excel_count_table_tra3 <- excel_count_table_tra2 %>% 
  mutate(nkt_trav11_traj18 = if_else(
    (grepl(allVHitsWithScore, pattern = "TRAV11") & 
       (grepl(allJHitsWithScore, pattern = "TRAJ18"))),"yes","no"))

excel_count_table_tra4 <- excel_count_table_tra3 %>% 
  filter(nkt_trav11_traj18 == "no")

count_table_tra4 <- as.matrix(excel_count_table_tra4[,5:32])
rownames(count_table_tra4) <- excel_count_table_tra4$aaSeqCDR3

tra4_norm <- scale(count_table_tra4, center=FALSE, scale=colSums(count_table_tra4))

prop.table.tra <- cbind(tra4_norm, 
                        excel_count_table_tra4 %>% 
                          select(-starts_with("CD")) ) 

prop.table.tra2 <- prop.table.tra %>% 
  dplyr::select(29, 31, 30, 32, 1:28)

prop.table.tra2 %>% head

##                     aaSeqCDR3 allJHitsWithScore allDHitsWithScore
## CAASASSGSWQLIF CAASASSGSWQLIF  TRAJ22*00(303.6)              <NA>
## CAASNMGYKLTF     CAASNMGYKLTF   TRAJ9*00(284.4)              <NA>
## CAVSASSGSWQLIF CAVSASSGSWQLIF  TRAJ22*00(304.2)              <NA>
## CAASDNYAQGLTF   CAASDNYAQGLTF    TRAJ26*00(284)              <NA>
## CALSDRYNQGKLIF CALSDRYNQGKLIF  TRAJ23*00(272.3)              <NA>
## CAASDDTNAYKVIF CAASDDTNAYKVIF  TRAJ30*00(295.7)              <NA>
##                                                                                                   allVHitsWithScore
## CAASASSGSWQLIF TRAV14N-2*00(1026.1),TRAV14D-2*00(1017.8),TRAV14D-1*00(973.4),TRAV14N-1*00(973.4),TRAV14-2*00(944.4)
## CAASNMGYKLTF                                                                   TRAV7-2*00(731.5),TRAV7D-2*00(731.5)
## CAVSASSGSWQLIF                                              TRAV3D-3*00(975.2),TRAV3N-3*00(975.2),TRAV3-3*00(944.1)
## CAASDNYAQGLTF                       TRAV14D-1*00(878.2),TRAV14N-1*00(878.2),TRAV14D-2*00(848.2),TRAV14N-2*00(848.2)
## CALSDRYNQGKLIF                                                            TRAV12D-3*00(1377.3),TRAV12N-3*00(1377.3)
## CAASDDTNAYKVIF                                                                                   TRAV14-1*00(966.7)
##                CD4 Lymph nodes CA Exp01 CD4 Lymph nodes CA Exp02
## CAASASSGSWQLIF             2.727925e-03             0.0035273369
## CAASNMGYKLTF               1.148600e-03             0.0016166961
## CAVSASSGSWQLIF             7.178751e-04             0.0007348618
## CAASDNYAQGLTF              2.871500e-04             0.0000000000
## CALSDRYNQGKLIF             7.178751e-05             0.0001469724
## CAASDDTNAYKVIF             0.000000e+00             0.0000000000
##                CD4 Lymph nodes CA Exp03 CD4 Lymph nodes CAKR Exp02
## CAASASSGSWQLIF             3.841094e-03               0.0027958993
## CAASNMGYKLTF               1.104972e-03               0.0013979497
## CAVSASSGSWQLIF             3.157064e-04               0.0004659832
## CAASDNYAQGLTF              3.157064e-04               0.0007455732
## CALSDRYNQGKLIF             4.209419e-04               0.0000000000
## CAASDDTNAYKVIF             5.261773e-05               0.0003727866
##                CD4 Lymph nodes CAKR Exp03 CD4 Lymph nodes WT Exp02
## CAASASSGSWQLIF               2.668321e-03             0.0035501049
## CAASNMGYKLTF                 8.687558e-04             0.0008068420
## CAVSASSGSWQLIF               5.584859e-04             0.0002420526
## CAASDNYAQGLTF                1.861620e-04             0.0001613684
## CALSDRYNQGKLIF               6.205399e-05             0.0000806842
## CAASDDTNAYKVIF               1.241080e-04             0.0000000000
##                CD4 Lymph nodes WT Exp03 CD4 Thymus CA Exp01 CD4 Thymus CA Exp03
## CAASASSGSWQLIF             3.291020e-03        0.0028328612        0.0022079929
## CAASNMGYKLTF               1.097007e-03        0.0012876642        0.0013247958
## CAVSASSGSWQLIF             2.611921e-04        0.0005150657        0.0003679988
## CAASDNYAQGLTF              1.567152e-04        0.0000000000        0.0003679988
## CALSDRYNQGKLIF             5.223842e-05        0.0000000000        0.0000000000
## CAASDDTNAYKVIF             1.567152e-04        0.0002575328        0.0000000000
##                CD4 Thymus CAKR Exp01 CD4 Thymus CAKR Exp02
## CAASASSGSWQLIF          0.0013199578          0.0037400655
## CAASNMGYKLTF            0.0013199578          0.0000000000
## CAVSASSGSWQLIF          0.0000000000          0.0023375409
## CAASDNYAQGLTF           0.0005279831          0.0000000000
## CALSDRYNQGKLIF          0.0000000000          0.0000000000
## CAASDDTNAYKVIF          0.0000000000          0.0004675082
##                CD4 Thymus CAKR Exp03 CD4 Thymus WT Exp02 CD4 Thymus WT Exp03
## CAASASSGSWQLIF          0.0033652602        0.0025633010        2.775089e-03
## CAASNMGYKLTF            0.0001294331        0.0013754298        5.808325e-04
## CAVSASSGSWQLIF          0.0005177323        0.0005001563        3.872217e-04
## CAASDNYAQGLTF           0.0000000000        0.0003125977        3.872217e-04
## CALSDRYNQGKLIF          0.0001294331        0.0000000000        6.453695e-05
## CAASDDTNAYKVIF          0.0000000000        0.0003751172        1.936108e-04
##                CD8 Lymph nodes CA Exp02 CD8 Lymph nodes CA Exp03
## CAASASSGSWQLIF             0.0099369387             0.0093881515
## CAASNMGYKLTF               0.0018154023             0.0020233085
## CAVSASSGSWQLIF             0.0021975922             0.0018614438
## CAASDNYAQGLTF              0.0009554749             0.0009711881
## CALSDRYNQGKLIF             0.0011465698             0.0008902557
## CAASDDTNAYKVIF             0.0012421173             0.0008093234
##                CD8 Lymph nodes CAKR Exp02 CD8 Lymph nodes CAKR Exp03
## CAASASSGSWQLIF               0.0080917874               0.0096842588
## CAASNMGYKLTF                 0.0019323671               0.0022348289
## CAVSASSGSWQLIF               0.0008454106               0.0020056157
## CAASDNYAQGLTF                0.0007246377               0.0009741562
## CALSDRYNQGKLIF               0.0016908213               0.0015471893
## CAASDDTNAYKVIF               0.0006038647               0.0004011231
##                CD8 Lymph nodes WT Exp01 CD8 Lymph nodes WT Exp02
## CAASASSGSWQLIF              0.009602698              0.011932808
## CAASNMGYKLTF                0.002492303              0.001946123
## CAVSASSGSWQLIF              0.001905879              0.001894909
## CAASDNYAQGLTF               0.001612667              0.001177917
## CALSDRYNQGKLIF              0.003665152              0.003021612
## CAASDDTNAYKVIF              0.001319455              0.000870634
##                CD8 Lymph nodes WT Exp03 CD8 Thymus CA Exp01 CD8 Thymus CA Exp03
## CAASASSGSWQLIF             0.0097454014        0.0062111801        0.0073130455
## CAASNMGYKLTF               0.0021318066        0.0014614541        0.0025163167
## CAVSASSGSWQLIF             0.0019490803        0.0014614541        0.0008649839
## CAASDNYAQGLTF              0.0012790839        0.0010960906        0.0004718094
## CALSDRYNQGKLIF             0.0024363503        0.0003653635        0.0005504443
## CAASDDTNAYKVIF             0.0007918139        0.0007307271        0.0009436188
##                CD8 Thymus CAKR Exp02 CD8 Thymus CAKR Exp03 CD8 Thymus WT Exp01
## CAASASSGSWQLIF          0.0084643289          0.0065963061        0.0088170463
## CAASNMGYKLTF            0.0008061266          0.0015077271        0.0020992967
## CAVSASSGSWQLIF          0.0016122531          0.0009423294        0.0019943319
## CAASDNYAQGLTF           0.0012091898          0.0011307953        0.0015744726
## CALSDRYNQGKLIF          0.0004030633          0.0005653977        0.0016794374
## CAASDDTNAYKVIF          0.0016122531          0.0016961930        0.0007347539
##                CD8 Thymus WT Exp02 CD8 Thymus WT Exp03
## CAASASSGSWQLIF        0.0053846154         0.007650815
## CAASNMGYKLTF          0.0015384615         0.001761339
## CAVSASSGSWQLIF        0.0015384615         0.001541171
## CAASDNYAQGLTF         0.0023076923         0.001431088
## CALSDRYNQGKLIF        0.0019230769         0.001155878
## CAASDDTNAYKVIF        0.0003846154         0.001265962

ExpData(data=prop.table.tra2, type=2)

##    Index              Variable_Name Variable_Type Sample_n Missing_Count
## 1      1                  aaSeqCDR3     character   114630             0
## 2      2          allJHitsWithScore     character   114630             0
## 3      3          allDHitsWithScore     character     4769        109861
## 4      4          allVHitsWithScore     character   114630             0
## 5      5   CD4 Lymph nodes CA Exp01       numeric   114630             0
## 6      6   CD4 Lymph nodes CA Exp02       numeric   114630             0
## 7      7   CD4 Lymph nodes CA Exp03       numeric   114630             0
## 8      8 CD4 Lymph nodes CAKR Exp02       numeric   114630             0
## 9      9 CD4 Lymph nodes CAKR Exp03       numeric   114630             0
## 10    10   CD4 Lymph nodes WT Exp02       numeric   114630             0
## 11    11   CD4 Lymph nodes WT Exp03       numeric   114630             0
## 12    12        CD4 Thymus CA Exp01       numeric   114630             0
## 13    13        CD4 Thymus CA Exp03       numeric   114630             0
## 14    14      CD4 Thymus CAKR Exp01       numeric   114630             0
## 15    15      CD4 Thymus CAKR Exp02       numeric   114630             0
## 16    16      CD4 Thymus CAKR Exp03       numeric   114630             0
## 17    17        CD4 Thymus WT Exp02       numeric   114630             0
## 18    18        CD4 Thymus WT Exp03       numeric   114630             0
## 19    19   CD8 Lymph nodes CA Exp02       numeric   114630             0
## 20    20   CD8 Lymph nodes CA Exp03       numeric   114630             0
## 21    21 CD8 Lymph nodes CAKR Exp02       numeric   114630             0
## 22    22 CD8 Lymph nodes CAKR Exp03       numeric   114630             0
## 23    23   CD8 Lymph nodes WT Exp01       numeric   114630             0
## 24    24   CD8 Lymph nodes WT Exp02       numeric   114630             0
## 25    25   CD8 Lymph nodes WT Exp03       numeric   114630             0
## 26    26        CD8 Thymus CA Exp01       numeric   114630             0
## 27    27        CD8 Thymus CA Exp03       numeric   114630             0
## 28    28      CD8 Thymus CAKR Exp02       numeric   114630             0
## 29    29      CD8 Thymus CAKR Exp03       numeric   114630             0
## 30    30        CD8 Thymus WT Exp01       numeric   114630             0
## 31    31        CD8 Thymus WT Exp02       numeric   114630             0
## 32    32        CD8 Thymus WT Exp03       numeric   114630             0
##    Per_of_Missing No_of_distinct_values
## 1           0.000                114630
## 2           0.000                  2972
## 3           0.958                   110
## 4           0.000                 31170
## 5           0.000                    27
## 6           0.000                    21
## 7           0.000                    36
## 8           0.000                    25
## 9           0.000                    34
## 10          0.000                    28
## 11          0.000                    34
## 12          0.000                    12
## 13          0.000                    29
## 14          0.000                    11
## 15          0.000                    10
## 16          0.000                    18
## 17          0.000                    28
## 18          0.000                    25
## 19          0.000                    21
## 20          0.000                    23
## 21          0.000                    17
## 22          0.000                    25
## 23          0.000                    26
## 24          0.000                    31
## 25          0.000                    30
## 26          0.000                     9
## 27          0.000                    23
## 28          0.000                     9
## 29          0.000                    11
## 30          0.000                    22
## 31          0.000                     9
## 32          0.000                    30

Prop table TRB

excel_count_table_trb3 <- excel_count_table_trb2 %>% 
  mutate(nkt_trav11_traj18 = "no")

count_table_trb4 <- as.matrix(excel_count_table_trb3[,5:32])
rownames(count_table_trb4) <- excel_count_table_trb3$aaSeqCDR3


trb4_norm <- scale(count_table_trb4, center=FALSE, scale=colSums(count_table_trb4))

prop.table.trb <- cbind(trb4_norm, excel_count_table_trb %>% select(-starts_with("CD")) ) 
colnames(prop.table.trb)

##  [1] "CD4 Lymph nodes CA Exp01"   "CD4 Lymph nodes CA Exp02"  
##  [3] "CD4 Lymph nodes CA Exp03"   "CD4 Lymph nodes CAKR Exp02"
##  [5] "CD4 Lymph nodes CAKR Exp03" "CD4 Lymph nodes WT Exp02"  
##  [7] "CD4 Lymph nodes WT Exp03"   "CD4 Thymus CA Exp01"       
##  [9] "CD4 Thymus CA Exp03"        "CD4 Thymus CAKR Exp01"     
## [11] "CD4 Thymus CAKR Exp02"      "CD4 Thymus CAKR Exp03"     
## [13] "CD4 Thymus WT Exp02"        "CD4 Thymus WT Exp03"       
## [15] "CD8 Lymph nodes CA Exp02"   "CD8 Lymph nodes CA Exp03"  
## [17] "CD8 Lymph nodes CAKR Exp02" "CD8 Lymph nodes CAKR Exp03"
## [19] "CD8 Lymph nodes WT Exp01"   "CD8 Lymph nodes WT Exp02"  
## [21] "CD8 Lymph nodes WT Exp03"   "CD8 Thymus CA Exp01"       
## [23] "CD8 Thymus CA Exp03"        "CD8 Thymus CAKR Exp02"     
## [25] "CD8 Thymus CAKR Exp03"      "CD8 Thymus WT Exp01"       
## [27] "CD8 Thymus WT Exp02"        "CD8 Thymus WT Exp03"       
## [29] "aaSeqCDR3"                  "allVHitsWithScore"         
## [31] "allDHitsWithScore"          "allJHitsWithScore"

prop.table.trb2 <- prop.table.trb %>% dplyr::select(29, 31, 30, 32, 1:28)


prop.table.trb2 %>% head

##                     aaSeqCDR3 allDHitsWithScore
## CASSDSAETLYF     CASSDSAETLYF              <NA>
## CASSDAEQFF         CASSDAEQFF              <NA>
## CASSDAGYEQYF     CASSDAGYEQYF              <NA>
## CASSDWGGYAEQFF CASSDWGGYAEQFF      TRBD2*00(45)
## CASSDSGGQDTQYF CASSDSGGQDTQYF      TRBD2*00(35)
## CASRDRNTEVFF     CASRDRNTEVFF      TRBD1*00(30)
##                                    allVHitsWithScore allJHitsWithScore
## CASSDSAETLYF   TRBV13-3*00(755.1),TRBV13-1*00(664.5)   TRBJ2-3*00(245)
## CASSDAEQFF                        TRBV13-1*00(819.6) TRBJ2-1*00(219.2)
## CASSDAGYEQYF                     TRBV13-1*00(1283.8)   TRBJ2-7*00(215)
## CASSDWGGYAEQFF                      TRBV13-1*00(676)   TRBJ2-1*00(235)
## CASSDSGGQDTQYF                    TRBV13-3*00(886.3)   TRBJ2-5*00(235)
## CASRDRNTEVFF                        TRBV13-3*00(979) TRBJ1-1*00(232.7)
##                CD4 Lymph nodes CA Exp01 CD4 Lymph nodes CA Exp02
## CASSDSAETLYF               1.729847e-04             5.154108e-05
## CASSDAEQFF                 0.000000e+00             0.000000e+00
## CASSDAGYEQYF               0.000000e+00             5.154108e-05
## CASSDWGGYAEQFF             7.413631e-05             5.154108e-05
## CASSDSGGQDTQYF             2.471210e-05             0.000000e+00
## CASRDRNTEVFF               1.482726e-04             5.154108e-05
##                CD4 Lymph nodes CA Exp03 CD4 Lymph nodes CAKR Exp02
## CASSDSAETLYF               1.141010e-04               3.152287e-05
## CASSDAEQFF                 9.508415e-05               0.000000e+00
## CASSDAGYEQYF               3.803366e-05               0.000000e+00
## CASSDWGGYAEQFF             1.901683e-05               1.260915e-04
## CASSDSGGQDTQYF             3.803366e-05               3.152287e-05
## CASRDRNTEVFF               5.705049e-05               9.456861e-05
##                CD4 Lymph nodes CAKR Exp03 CD4 Lymph nodes WT Exp02
## CASSDSAETLYF                 2.747190e-04             2.637618e-05
## CASSDAEQFF                   2.289325e-05             5.275235e-05
## CASSDAGYEQYF                 6.867975e-05             2.637618e-04
## CASSDWGGYAEQFF               0.000000e+00             0.000000e+00
## CASSDSGGQDTQYF               0.000000e+00             0.000000e+00
## CASRDRNTEVFF                 0.000000e+00             0.000000e+00
##                CD4 Lymph nodes WT Exp03 CD4 Thymus CA Exp01 CD4 Thymus CA Exp03
## CASSDSAETLYF               2.293368e-04        2.366397e-04        6.682259e-05
## CASSDAEQFF                 2.293368e-05        7.887991e-05        4.454839e-05
## CASSDAGYEQYF               2.293368e-05        3.549596e-04        1.113710e-04
## CASSDWGGYAEQFF             0.000000e+00        1.577598e-04        2.227420e-05
## CASSDSGGQDTQYF             0.000000e+00        0.000000e+00        0.000000e+00
## CASRDRNTEVFF               6.880103e-05        0.000000e+00        4.454839e-05
##                CD4 Thymus CAKR Exp01 CD4 Thymus CAKR Exp02
## CASSDSAETLYF            8.731718e-05                     0
## CASSDAEQFF              0.000000e+00                     0
## CASSDAGYEQYF            4.365859e-05                     0
## CASSDWGGYAEQFF          0.000000e+00                     0
## CASSDSGGQDTQYF          0.000000e+00                     0
## CASRDRNTEVFF            0.000000e+00                     0
##                CD4 Thymus CAKR Exp03 CD4 Thymus WT Exp02 CD4 Thymus WT Exp03
## CASSDSAETLYF            0.0000000000        4.972527e-05        2.538554e-04
## CASSDAEQFF              0.0000000000        2.486263e-05        3.173193e-05
## CASSDAGYEQYF            0.0000300075        2.486263e-05        0.000000e+00
## CASSDWGGYAEQFF          0.0000000000        2.486263e-05        3.173193e-05
## CASSDSGGQDTQYF          0.0000000000        2.486263e-05        0.000000e+00
## CASRDRNTEVFF            0.0002100525        0.000000e+00        3.173193e-05
##                CD8 Lymph nodes CA Exp02 CD8 Lymph nodes CA Exp03
## CASSDSAETLYF               3.846495e-04             3.699984e-04
## CASSDAEQFF                 2.662958e-04             1.849992e-04
## CASSDAGYEQYF               3.254727e-04             2.642846e-04
## CASSDWGGYAEQFF             1.183537e-04             2.642846e-04
## CASSDSGGQDTQYF             5.917685e-05             5.285692e-05
## CASRDRNTEVFF               2.367074e-04             5.285692e-05
##                CD8 Lymph nodes CAKR Exp02 CD8 Lymph nodes CAKR Exp03
## CASSDSAETLYF                 3.195909e-04               2.544113e-04
## CASSDAEQFF                   1.452686e-04               7.268895e-05
## CASSDAGYEQYF                 3.776984e-04               2.544113e-04
## CASSDWGGYAEQFF               9.878265e-04               4.179614e-04
## CASSDSGGQDTQYF               2.905372e-05               7.268895e-05
## CASRDRNTEVFF                 1.452686e-04               1.272057e-04
##                CD8 Lymph nodes WT Exp01 CD8 Lymph nodes WT Exp02
## CASSDSAETLYF               0.0005254621             3.488707e-04
## CASSDAEQFF                 0.0002741541             1.365146e-04
## CASSDAGYEQYF               0.0005483082             6.219000e-04
## CASSDWGGYAEQFF             0.0003198465             2.730293e-04
## CASSDSGGQDTQYF             0.0003198465             4.550488e-05
## CASRDRNTEVFF               0.0002056156             4.550488e-05
##                CD8 Lymph nodes WT Exp03 CD8 Thymus CA Exp01 CD8 Thymus CA Exp03
## CASSDSAETLYF               0.0002286917        0.0003316475        1.856493e-04
## CASSDAEQFF                 0.0001829533        0.0002487356        9.282465e-05
## CASSDAGYEQYF               0.0005031216        0.0003316475        1.856493e-04
## CASSDWGGYAEQFF             0.0002058225        0.0001658237        3.094155e-05
## CASSDSGGQDTQYF             0.0000000000        0.0000000000        3.094155e-05
## CASRDRNTEVFF               0.0001372150        0.0000000000        1.237662e-04
##                CD8 Thymus CAKR Exp02 CD8 Thymus CAKR Exp03 CD8 Thymus WT Exp01
## CASSDSAETLYF            0.0000000000          0.0000000000        1.804457e-04
## CASSDAEQFF              0.0002597628          0.0002214839        3.308171e-04
## CASSDAGYEQYF            0.0000000000          0.0001661130        4.210400e-04
## CASSDWGGYAEQFF          0.0003463503          0.0002214839        2.706686e-04
## CASSDSGGQDTQYF          0.0000000000          0.0001107420        6.014857e-05
## CASRDRNTEVFF            0.0000000000          0.0000000000        1.202971e-04
##                CD8 Thymus WT Exp02 CD8 Thymus WT Exp03
## CASSDSAETLYF          4.479484e-04        2.347268e-04
## CASSDAEQFF            2.687690e-04        8.535518e-05
## CASSDAGYEQYF          3.583587e-04        2.774043e-04
## CASSDWGGYAEQFF        2.687690e-04        6.401639e-05
## CASSDSGGQDTQYF        0.000000e+00        2.133880e-05
## CASRDRNTEVFF          8.958968e-05        1.066940e-04

ExpData(data=prop.table.trb2, type=2)

##    Index              Variable_Name Variable_Type Sample_n Missing_Count
## 1      1                  aaSeqCDR3     character   426800             0
## 2      2          allDHitsWithScore     character   331672         95128
## 3      3          allVHitsWithScore     character   426800             0
## 4      4          allJHitsWithScore     character   426800             0
## 5      5   CD4 Lymph nodes CA Exp01       numeric   426800             0
## 6      6   CD4 Lymph nodes CA Exp02       numeric   426800             0
## 7      7   CD4 Lymph nodes CA Exp03       numeric   426800             0
## 8      8 CD4 Lymph nodes CAKR Exp02       numeric   426800             0
## 9      9 CD4 Lymph nodes CAKR Exp03       numeric   426800             0
## 10    10   CD4 Lymph nodes WT Exp02       numeric   426800             0
## 11    11   CD4 Lymph nodes WT Exp03       numeric   426800             0
## 12    12        CD4 Thymus CA Exp01       numeric   426800             0
## 13    13        CD4 Thymus CA Exp03       numeric   426800             0
## 14    14      CD4 Thymus CAKR Exp01       numeric   426800             0
## 15    15      CD4 Thymus CAKR Exp02       numeric   426800             0
## 16    16      CD4 Thymus CAKR Exp03       numeric   426800             0
## 17    17        CD4 Thymus WT Exp02       numeric   426800             0
## 18    18        CD4 Thymus WT Exp03       numeric   426800             0
## 19    19   CD8 Lymph nodes CA Exp02       numeric   426800             0
## 20    20   CD8 Lymph nodes CA Exp03       numeric   426800             0
## 21    21 CD8 Lymph nodes CAKR Exp02       numeric   426800             0
## 22    22 CD8 Lymph nodes CAKR Exp03       numeric   426800             0
## 23    23   CD8 Lymph nodes WT Exp01       numeric   426800             0
## 24    24   CD8 Lymph nodes WT Exp02       numeric   426800             0
## 25    25   CD8 Lymph nodes WT Exp03       numeric   426800             0
## 26    26        CD8 Thymus CA Exp01       numeric   426800             0
## 27    27        CD8 Thymus CA Exp03       numeric   426800             0
## 28    28      CD8 Thymus CAKR Exp02       numeric   426800             0
## 29    29      CD8 Thymus CAKR Exp03       numeric   426800             0
## 30    30        CD8 Thymus WT Exp01       numeric   426800             0
## 31    31        CD8 Thymus WT Exp02       numeric   426800             0
## 32    32        CD8 Thymus WT Exp03       numeric   426800             0
##    Per_of_Missing No_of_distinct_values
## 1           0.000                426800
## 2           0.223                   214
## 3           0.000                 23763
## 4           0.000                  2168
## 5           0.000                    29
## 6           0.000                    21
## 7           0.000                    33
## 8           0.000                    20
## 9           0.000                    38
## 10          0.000                    30
## 11          0.000                    25
## 12          0.000                    24
## 13          0.000                    42
## 14          0.000                    19
## 15          0.000                    14
## 16          0.000                    39
## 17          0.000                    17
## 18          0.000                    15
## 19          0.000                    24
## 20          0.000                    25
## 21          0.000                    24
## 22          0.000                    32
## 23          0.000                    22
## 24          0.000                    32
## 25          0.000                    23
## 26          0.000                     9
## 27          0.000                    20
## 28          0.000                    14
## 29          0.000                    14
## 30          0.000                    16
## 31          0.000                     9
## 32          0.000                    20

Gene segment usage analysis

immdata_tra <- readRDS("immdata_tra.rds")
immdata_trb <- readRDS("immdata_trb.rds")

### Thymus

cd4_thymus <- geneUsage(repFilter(immdata_tra, 
                                 .method = "by.meta", 
                                 .query = list(Organ = include('Thymus'), Cell_type = include('CD4')))$data, .norm = T, .ambig = "maj", .quant = "count", .gene = "musmus.trav")

vis(cd4_thymus, .by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_tra$meta) + ggtitle("TRA Thymus CD4") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70"))

#ggsave("./final_fig/gene_usage_new/cd4_thymus_tra_just1segment.png", width = 60, height = 12, units = "cm") 
#ggsave("./final_fig/gene_usage_new/cd4_thymus_tra_just1segment.svg", width = 60, height = 12, units = "cm") 


### LN
cd4_ln <- geneUsage(repFilter(immdata_tra, 
                                 .method = "by.meta", 
                                 .query = list(Organ = exclude('Thymus'), Cell_type = include('CD4')))$data, .norm = T, .ambig = "maj", .quant = "count", .gene = "musmus.trav")

vis(cd4_ln, .by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_tra$meta) + ggtitle("TRA LN CD4") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70"))

#ggsave("./final_fig/gene_usage_new/cd4_ln_tra_just1segment.png", width = 60, height = 12, units = "cm")
#ggsave("./final_fig/gene_usage_new/cd4_ln_tra_just1segment.svg", width = 60, height = 12, units = "cm")

cd8_thymus <- geneUsage(repFilter(immdata_tra, 
                                 .method = "by.meta", 
                                 .query = list(Organ = include('Thymus'), Cell_type = include('CD8')))$data, .norm = T, .ambig = "maj", .quant = "count", .gene = "musmus.trav")

vis(cd8_thymus, .by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_tra$meta) + ggtitle("TRA Thymus CD8") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70"))

#ggsave("./final_fig/gene_usage_new/cd8_thymus_tra_just1segment.png", width = 60, height = 12, units = "cm")
#ggsave("./final_fig/gene_usage_new/cd8_thymus_tra_just1segment.svg", width = 60, height = 12, units = "cm")


cd8_ln <- geneUsage(repFilter(immdata_tra, 
                                 .method = "by.meta", 
                                 .query = list(Organ = exclude('Thymus'), Cell_type = include('CD8')))$data, .norm = T, .ambig = "maj", .quant = "count", .gene = "musmus.trav")

vis(cd8_ln, .by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_tra$meta) + ggtitle("TRA LN CD8") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70"))

#ggsave("./final_fig/gene_usage_new/cd8_ln_tra_just1segment.png", width = 60, height = 12, units = "cm")
#ggsave("./final_fig/gene_usage_new/cd8_ln_tra_just1segment.svg", width = 60, height = 12, units = "cm")

### Thymus

cd4_thymus <- geneUsage(repFilter(immdata_trb, 
                                 .method = "by.meta", 
                                 .query = list(Organ = include('Thymus'), Cell_type = include('CD4')))$data, .norm = T, .ambig = "maj", .quant = "count", .gene = "musmus.trbv")

vis(cd4_thymus, .by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_trb$meta, .test = F) + ggtitle("TRB Thymus CD4") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70")) 

#ggsave("./final_fig/gene_usage_new/cd4_thymus_trb_just1segment.png", width = 20, height = 10, units = "cm") 
#ggsave("./final_fig/gene_usage_new/cd4_thymus_trb_just1segment.svg", width = 20, height = 10, units = "cm")

### LN
cd4_ln <- geneUsage(repFilter(immdata_trb, 
                                 .method = "by.meta", 
                                 .query = list(Organ = exclude('Thymus'), Cell_type = include('CD4')))$data, .norm = T, .ambig = "maj", .quant = "count", .gene = "musmus.trbv")

vis(cd4_ln, .by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_trb$meta, .test = F) + ggtitle("TRB LN CD4") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70"))

#ggsave("./final_fig/gene_usage_new/cd4_ln_trb_just1segment.png", width = 20, height = 10, units = "cm")
#ggsave("./final_fig/gene_usage_new/cd4_ln_trb_just1segment.svg", width = 20, height = 10, units = "cm")


cd8_thymus <- geneUsage(repFilter(immdata_trb, 
                                 .method = "by.meta", 
                                 .query = list(Organ = include('Thymus'), Cell_type = include('CD8')))$data, .norm = T, .ambig = "maj", .quant = "count", .gene = "musmus.trbv")

vis(cd8_thymus, .by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_trb$meta, .test = F) + ggtitle("TRB Thymus CD8") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70"))

#ggsave("./final_fig/gene_usage_new/cd8_thymus_trb_just1segment.png", width = 20, height = 10, units = "cm")
#ggsave("./final_fig/gene_usage_new/cd8_thymus_trb_just1segment.svg", width = 20, height = 10, units = "cm")


cd8_ln <- geneUsage(repFilter(immdata_trb, 
                                 .method = "by.meta", 
                                 .query = list(Organ = exclude('Thymus'), Cell_type = include('CD8')))$data, .norm = T, .ambig = "maj", .quant = "count", .gene = "musmus.trbv")

vis(cd8_ln, .by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_trb$meta, .test = F) + ggtitle("TRB LN CD8") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70"))

#ggsave("./final_fig/gene_usage_new/cd8_ln_trb_just1segment.png", width = 20, height = 10, units = "cm")
#ggsave("./final_fig/gene_usage_new/cd8_ln_trb_just1segment.svg", width = 20, height = 10, units = "cm")

Analysis of NKT cells

Next, we will identify gene segments that are typical for invariant NKT cells. These segments are TRAV11 and TRAJ18 for TRA and TRBV1, TRBV13-2 and TRBV29 for TRB. Please, note that some of the included TRB chains are used by conventional cells as well.

# NKT analysis
merged_repertoires <- merged_repertoires %>% 
  mutate(is_nkt = if_else(
    (grepl(allVHitsWithScore, pattern = "TRAV11")) & (grepl(allJHitsWithScore, pattern = "TRAJ18")) |
    (grepl(allVHitsWithScore, pattern = "TRBV13-2")) |
      (grepl(allVHitsWithScore, pattern = "TRBV1\\*")) |
      (grepl(allVHitsWithScore, pattern = "TRBV29"))  ,"yes","no")) %>% 
  mutate(nkt_trav11_traj18 = if_else(
    (grepl(allVHitsWithScore, pattern = "TRAV11") & (grepl(allJHitsWithScore, pattern = "TRAJ18"))),"yes","no")) %>% mutate(nkt_trbv13_2 = if_else(
    (grepl(allVHitsWithScore, pattern = "TRBV13-2")),"yes","no")) %>% mutate(nkt_trbv29 = if_else(
    (grepl(allVHitsWithScore, pattern = "TRBV29")),"yes","no")) %>% mutate(nkt_trbv1 = if_else(
    (grepl(allVHitsWithScore, pattern = "TRBV1\\*")),"yes","no"))

md <- md %>% select(Exp, Organ, Cell_type, Mouse_strain, num_id) %>% mutate_at(vars("num_id"), as.numeric)

# All TRB NKT
nkt_table_trb <- merged_repertoires %>% 
   filter(chain == "TRB") %>% 
 mutate(new_name = paste(Exp, Organ, Cell_type, Mouse_strain)) %>% 
  dplyr::select(new_name, cloneCount, is_nkt, num_id) %>% 
  uncount(cloneCount) %>% 
  group_by(num_id, is_nkt) %>% 
    summarise(n = n()) %>% 
mutate(freq = n / sum(n)) %>% dplyr::filter(is_nkt == "yes") %>% 
  ungroup %>% 
  left_join(md) %>% 
  unique %>%
   mutate(sample_type = paste(Cell_type, Organ, Mouse_strain)) 

# TRAV11 TRAJ18
nkt_table_trav11_traj18 <- merged_repertoires %>% 
   filter(chain == "TRA") %>% 
 mutate(new_name = paste(Exp, Organ, Cell_type, Mouse_strain)) %>% 
  dplyr::select(new_name, cloneCount, nkt_trav11_traj18, num_id) %>% 
  uncount(cloneCount) %>% 
  group_by(num_id, nkt_trav11_traj18) %>% 
    summarise(n = n()) %>% 
mutate(freq = n / sum(n)) %>% dplyr::filter(nkt_trav11_traj18 == "yes") %>% 
  ungroup %>% 
  left_join(md) %>% 
  unique %>%
   mutate(sample_type = paste(Cell_type, Organ, Mouse_strain)) 

# TRBV1
nkt_table_trbv1 <- merged_repertoires %>% 
   filter(chain == "TRB") %>% 
 mutate(new_name = paste(Exp, Organ, Cell_type, Mouse_strain)) %>% 
  dplyr::select(new_name, cloneCount, nkt_trbv1, num_id) %>% 
  uncount(cloneCount) %>% 
  group_by(num_id, nkt_trbv1) %>% 
    summarise(n = n()) %>% 
mutate(freq = n / sum(n)) %>% dplyr::filter(nkt_trbv1 == "yes") %>% 
  ungroup %>% 
  left_join(md) %>% 
  unique %>%
   mutate(sample_type = paste(Cell_type, Organ, Mouse_strain)) 

# TRBV13-2
nkt_table_trbv13_2 <- merged_repertoires %>% 
   filter(chain == "TRB") %>% 
 mutate(new_name = paste(Exp, Organ, Cell_type, Mouse_strain)) %>% 
  dplyr::select(new_name, cloneCount, nkt_trbv13_2, num_id) %>% 
  uncount(cloneCount) %>% 
  group_by(num_id, nkt_trbv13_2) %>% 
    summarise(n = n()) %>% 
mutate(freq = n / sum(n)) %>% dplyr::filter(nkt_trbv13_2 == "yes") %>% 
  ungroup %>% 
  left_join(md) %>% 
  unique %>%
   mutate(sample_type = paste(Cell_type, Organ, Mouse_strain)) 


# TRBV29
nkt_table_trbv29 <- merged_repertoires %>% 
   filter(chain == "TRB") %>% 
 mutate(new_name = paste(Exp, Organ, Cell_type, Mouse_strain)) %>% 
  dplyr::select(new_name, cloneCount, nkt_trbv29, num_id) %>% 
  uncount(cloneCount) %>% 
  group_by(num_id, nkt_trbv29) %>% 
    summarise(n = n()) %>% 
mutate(freq = n / sum(n)) %>% dplyr::filter(nkt_trbv29 == "yes") %>% 
  ungroup %>% 
  left_join(md) %>% 
  unique %>%
   mutate(sample_type = paste(Cell_type, Organ, Mouse_strain)) 

levels_cd4 <- c("CD4 Thymus WT", "CD4 Thymus CA", "CD4 Thymus CAKR", "CD4 Lymph nodes WT", "CD4 Lymph nodes CA", "CD4 Lymph nodes CAKR")
levels_cd8 <- c("CD8 Thymus WT", "CD8 Thymus CA", "CD8 Thymus CAKR", "CD8 Lymph nodes WT", "CD8 Lymph nodes CA", "CD8 Lymph nodes CAKR")

nkt_table_trb$nkt_sample <- "all_nkt_trb"
nkt_table_trav11_traj18$nkt_sample <- "trav11_traj18"
nkt_table_trbv1$nkt_sample <- "trbv1"
nkt_table_trbv13_2$nkt_sample <- "trbv13_2"
nkt_table_trbv29$nkt_sample <- "trbv29"

nkt_table <- cbind(nkt_table_trb$num_id, nkt_table_trb$sample_type, nkt_table_trb$Exp, format(nkt_table_trb$freq, digits = 2), 
                   format(nkt_table_trav11_traj18$freq, digits = 2),
format(nkt_table_trbv1$freq, digits = 2), 
format(nkt_table_trbv13_2$freq, digits = 2),
                   format(nkt_table_trbv29$freq, digits = 2)) %>% as.data.frame

colnames(nkt_table) <- c("num_id","Sample","Exp","All TRB NKT", "TRAV11-TRAJ18", "TRBV1","TRBV13-2","TRBV29")
nkt_table <- nkt_table %>% mutate(Sample = paste(Sample, Exp)) %>% select(-num_id, -Exp) %>% arrange(Sample)

See the percentage of NKT gene segments in each sample:

kable(nkt_table, format = "html") %>%
  kable_styling(full_width = F, font_size = 11, 
                bootstrap_options = c("striped", "hover", "condensed", "responsive"))

Now we will plot the data for CD4 cells and for CD8 cells. Plots for TRA and TRB sequences are separated.

## CD4
p01 <- nkt_table_trb %>% 
  filter(Cell_type == "CD4") %>% 
  ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd4))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Mouse_strain)) + 
  ylab("Percentage of all chains") + 
  ggtitle("NKT segments in CD4 TRB") + 
  theme_classic() + 
  ggtheme() + 
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")
  
#ggsave("./final_fig/nkt/pct_all_nkt_trb_cd4.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_all_nkt_trb_cd4.eps", width = 8, height = 8, units = "cm")

## CD8
p02 <- nkt_table_trb %>% 
  filter(Cell_type == "CD8") %>% 
  ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd8))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Mouse_strain)) + 
  ylab("Percentage of all chains") + 
  ggtitle("NKT segments in CD8 TRB") + 
  theme_classic() + 
  ggtheme() + 
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")
  
#ggsave("./final_fig/nkt/pct_all_nkt_trb_cd8.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_all_nkt_trb_cd8.eps", width = 8, height = 8, units = "cm")

p01 + p02

## CD4
p03 <- nkt_table_trav11_traj18 %>% 
  filter(Cell_type == "CD4") %>% 
  ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd4))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Mouse_strain)) + 
  ylab("Percentage of all chains") + 
  ggtitle("TRAV11-TRAJ18 segments in CD4 cells") + 
  theme_classic() + 
  ggtheme() + 
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")
  
#ggsave("./final_fig/nkt/pct_nkt_trav11_traj18_tra_cd4.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_nkt_trav11_traj18_tra_cd4.eps", width = 8, height = 8, units = "cm")

## CD8
p04 <- nkt_table_trav11_traj18 %>% 
  filter(Cell_type == "CD8") %>% 
  ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd8))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Mouse_strain)) + 
  ylab("Percentage of all chains") + 
  ggtitle("TRAV11-TRAJ18 segments in CD8 cells") + 
  theme_classic() + 
  ggtheme() + 
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,1)) + xlab("")
  
#ggsave("./final_fig/nkt/pct_nkt_trav11_traj18_tra_cd8.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_nkt_trav11_traj18_tra_cd8.eps", width = 8, height = 8, units = "cm")

p03 + p04

## CD4
p05 <- nkt_table_trbv1 %>% 
  filter(Cell_type == "CD4") %>% 
  ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd4))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Mouse_strain)) + 
  ylab("Percentage of all chains") + 
  ggtitle("CD4 TRBV1") + 
  theme_classic() + 
  ggtheme() + 
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")
  
#ggsave("./final_fig/nkt/pct_nkt_trbv1_cd4.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_nkt_trbv1_cd4.eps", width = 8, height = 8, units = "cm")

## CD8
p06 <- nkt_table_trbv1 %>% 
  filter(Cell_type == "CD8") %>% 
  ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd8))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Mouse_strain)) + 
  ylab("Percentage of all chains") + 
  ggtitle("CD8 TRBV1") + 
  theme_classic() + 
  ggtheme() + 
  
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")
  
#ggsave("./final_fig/nkt/pct_nkt_trbv1_cd8.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_nkt_trbv1_cd8.eps", width = 8, height = 8, units = "cm")

p05 + p06

## CD4
p07 <- nkt_table_trbv13_2 %>% 
  filter(Cell_type == "CD4") %>% 
  ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd4))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Mouse_strain)) + 
  ylab("Percentage of all chains") + 
  ggtitle("TRBV13-2 segments in CD4 cells") + 
  theme_classic() + 
  ggtheme() + 
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")
  
#ggsave("./final_fig/nkt/pct_nkt_trbv13_2_cd4.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_nkt_trbv13_2_cd4.eps", width = 8, height = 8, units = "cm")

## CD8
p08 <- nkt_table_trbv13_2 %>% 
  filter(Cell_type == "CD8") %>% 
  ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd8))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Mouse_strain)) + 
  ylab("Percentage of all chains") + 
  ggtitle("TRBV13-2 segments in CD8 cells") + 
  theme_classic() + 
  ggtheme() + 
  
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")
  
#ggsave("./final_fig/nkt/pct_nkt_trbv13_2_cd8.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_nkt_trbv13_2_cd8.eps", width = 8, height = 8, units = "cm")

p07 + p08

## CD4
p09 <- nkt_table_trbv29 %>% 
  filter(Cell_type == "CD4") %>% 
  ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd4))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Mouse_strain)) + 
  ylab("Percentage of all chains") + 
  ggtitle("CD4 TRBV29") + 
  theme_classic() + 
  ggtheme() + 
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")
  
#ggsave("./final_fig/nkt/pct_nkt_trbv29_cd4.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_nkt_trbv29_cd4.eps", width = 8, height = 8, units = "cm")

## CD8
p10 <- nkt_table_trbv29 %>% 
  filter(Cell_type == "CD8") %>% 
  ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd8))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Mouse_strain)) + 
  ylab("Percentage of all chains") + 
  ggtitle("CD8 TRBV29") + 
  theme_classic() + 
  ggtheme() + 
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")
  
#ggsave("./final_fig/nkt/pct_nkt_trbv29_cd8.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_nkt_trbv29_cd8.eps", width = 8, height = 8, units = "cm")

p09 + p10

PCA

In this part we explore the similarity of TCR repertoires of different samples using principal component analysis (PCA).

We will calculate the distances from a filtered matrix of proportions, in which we filtered out the CDR3 sequences that are present in less than five CD4 or in less than five CD8 samples.

# TRA
keep_tra_cd4 <- excel_count_table_tra %>% 
  mutate(nkt_trav11_traj18 = if_else(
    (grepl(allVHitsWithScore, pattern = "TRAV11") | 
    (grepl(allJHitsWithScore, pattern = "TRAJ18"))),"yes","no")) %>% 
  filter(nkt_trav11_traj18 == "no") %>% 
  select(aaSeqCDR3, starts_with("CD4")) %>% 
  mutate_at(vars(starts_with("CD4")), .funs = binary) 
 
keep_tra_cd4$sum <- rowSums((keep_tra_cd4 %>% select(-aaSeqCDR3)))

keep_tra_cd4$keep <- ifelse(keep_tra_cd4$sum>4,1,0)
keep_tra_cd4$orig <- ifelse(keep_tra_cd4$sum>0,1,0)

keep_tra_cd4_sequences <- pull(keep_tra_cd4 %>% filter(keep == 1), aaSeqCDR3)

# TRB
keep_trb_cd4 <- excel_count_table_trb %>% select(aaSeqCDR3, starts_with("CD4")) %>%  mutate_at(vars(starts_with("CD4")), .funs = binary) 
 
keep_trb_cd4$sum <- rowSums((keep_trb_cd4 %>% select(-aaSeqCDR3)))

keep_trb_cd4$keep <- ifelse(keep_trb_cd4$sum>4,1,0)
keep_trb_cd4$orig <- ifelse(keep_trb_cd4$sum>0,1,0)

keep_trb_cd4_sequences <- pull(keep_trb_cd4 %>% filter(keep == 1), aaSeqCDR3)

# TRA
keep_tra_cd8 <- excel_count_table_tra %>% 
  mutate(nkt_trav11_traj18 = if_else(
    (grepl(allVHitsWithScore, pattern = "TRAV11") | 
    (grepl(allJHitsWithScore, pattern = "TRAJ18"))),"yes","no")) %>% 
  filter(nkt_trav11_traj18 == "no") %>% 
  select(aaSeqCDR3, starts_with("CD8")) %>% 
  mutate_at(vars(starts_with("CD8")), .funs = binary) 

keep_tra_cd8$sum <- rowSums((keep_tra_cd8 %>% select(-aaSeqCDR3)))

keep_tra_cd8$keep <- ifelse(keep_tra_cd8$sum>4,1,0)
keep_tra_cd8$orig <- ifelse(keep_tra_cd8$sum>0,1,0)

keep_tra_cd8_sequences <- pull(keep_tra_cd8 %>% filter(keep == 1), aaSeqCDR3)

# TRB
keep_trb_cd8 <- excel_count_table_trb %>% select(aaSeqCDR3, starts_with("CD8")) %>%  mutate_at(vars(starts_with("CD8")), .funs = binary) 
 
keep_trb_cd8$sum <- rowSums((keep_trb_cd8 %>% select(-aaSeqCDR3)))

keep_trb_cd8$keep <- ifelse(keep_trb_cd8$sum>4,1,0)
keep_trb_cd8$orig <- ifelse(keep_trb_cd8$sum>0,1,0)

keep_trb_cd8_sequences <- pull(keep_trb_cd8 %>% filter(keep == 1), aaSeqCDR3)


cdr3_count_table <- data.frame(Sample = c("CD4 TRA","CD4 TRB","CD8 TRA","CD8 TRB"),
                               `Count before filtering` = c(
                                 length(pull(keep_tra_cd4 %>% filter(orig == 1), aaSeqCDR3)),
                                 length(pull(keep_trb_cd4 %>% filter(orig == 1), aaSeqCDR3)),
                                 length(pull(keep_tra_cd8 %>% filter(orig == 1), aaSeqCDR3)),
                                 length(pull(keep_trb_cd8 %>% filter(orig == 1), aaSeqCDR3))),
                               `Count after filtering` = c(
                                 length(pull(keep_tra_cd4 %>% filter(keep == 1), aaSeqCDR3)),
                                 length(pull(keep_trb_cd4 %>% filter(keep == 1), aaSeqCDR3)),
                                 length(pull(keep_tra_cd8 %>% filter(keep == 1), aaSeqCDR3)),
                                 length(pull(keep_trb_cd8 %>% filter(keep == 1), aaSeqCDR3))))

kable(cdr3_count_table) %>%
  kable_styling(full_width = F, font_size = 11, 
                bootstrap_options = c("striped", "hover", "condensed", "responsive"))

# TRB
excel_count_table_trb3_filter <- excel_count_table_trb2  %>% filter(aaSeqCDR3 %in% keep_trb_cd4_sequences | aaSeqCDR3 %in% keep_trb_cd8_sequences) 

count_table_trb4_filter <- as.matrix(excel_count_table_trb3_filter[,5:32])
rownames(count_table_trb4_filter) <- excel_count_table_trb3_filter$aaSeqCDR3

trb4_norm_filter <- scale(count_table_trb4_filter, center=FALSE, scale=colSums(count_table_trb4_filter))

prop.table.trb_filter <- cbind(trb4_norm_filter, excel_count_table_trb3_filter %>% select(-starts_with("CD")) )

# TRA
excel_count_table_tra3_filter <- excel_count_table_tra3  %>% filter(aaSeqCDR3 %in% keep_tra_cd4_sequences | aaSeqCDR3 %in% keep_tra_cd8_sequences) %>% filter(nkt_trav11_traj18 == "no")

count_table_tra4_filter <- as.matrix(excel_count_table_tra3_filter[,5:32])
rownames(count_table_tra4_filter) <- excel_count_table_tra3_filter$aaSeqCDR3

tra4_norm_filter <- scale(count_table_tra4_filter, center=FALSE, scale=colSums(count_table_tra4_filter))

prop.table.tra_filter <- cbind(tra4_norm_filter, excel_count_table_tra3_filter %>% select(-starts_with("CD")) )

colnames(prop.table.tra_filter)

##  [1] "CD4 Lymph nodes CA Exp01"   "CD4 Lymph nodes CA Exp02"  
##  [3] "CD4 Lymph nodes CA Exp03"   "CD4 Lymph nodes CAKR Exp02"
##  [5] "CD4 Lymph nodes CAKR Exp03" "CD4 Lymph nodes WT Exp02"  
##  [7] "CD4 Lymph nodes WT Exp03"   "CD4 Thymus CA Exp01"       
##  [9] "CD4 Thymus CA Exp03"        "CD4 Thymus CAKR Exp01"     
## [11] "CD4 Thymus CAKR Exp02"      "CD4 Thymus CAKR Exp03"     
## [13] "CD4 Thymus WT Exp02"        "CD4 Thymus WT Exp03"       
## [15] "CD8 Lymph nodes CA Exp02"   "CD8 Lymph nodes CA Exp03"  
## [17] "CD8 Lymph nodes CAKR Exp02" "CD8 Lymph nodes CAKR Exp03"
## [19] "CD8 Lymph nodes WT Exp01"   "CD8 Lymph nodes WT Exp02"  
## [21] "CD8 Lymph nodes WT Exp03"   "CD8 Thymus CA Exp01"       
## [23] "CD8 Thymus CA Exp03"        "CD8 Thymus CAKR Exp02"     
## [25] "CD8 Thymus CAKR Exp03"      "CD8 Thymus WT Exp01"       
## [27] "CD8 Thymus WT Exp02"        "CD8 Thymus WT Exp03"       
## [29] "aaSeqCDR3"                  "allDHitsWithScore"         
## [31] "allJHitsWithScore"          "allVHitsWithScore"         
## [33] "nkt_trav11_traj18"

prop.table.tra2_filter.cd4 <- prop.table.tra_filter[,1:14]
prop.table.tra2_filter.cd4$sum <- rowSums(prop.table.tra2_filter.cd4)
prop.table.tra2_filter.cd4 <- prop.table.tra2_filter.cd4 %>% filter(sum >0) %>% select(-sum)

prop.table.trb2_filter.cd4 <- prop.table.trb_filter[,1:14]
prop.table.trb2_filter.cd4$sum <- rowSums(prop.table.trb2_filter.cd4)
prop.table.trb2_filter.cd4 <- prop.table.trb2_filter.cd4 %>% filter(sum >0) %>% select(-sum)

prop.table.tra2_filter.cd8 <- prop.table.tra_filter[,15:28]
prop.table.tra2_filter.cd8$sum <- rowSums(prop.table.tra2_filter.cd8)
prop.table.tra2_filter.cd8 <- prop.table.tra2_filter.cd8 %>% filter(sum >0) %>% select(-sum)

prop.table.trb2_filter.cd8 <- prop.table.trb_filter[,15:28]
prop.table.trb2_filter.cd8$sum <- rowSums(prop.table.trb2_filter.cd8)
prop.table.trb2_filter.cd8 <- prop.table.trb2_filter.cd8 %>% filter(sum >0) %>% select(-sum)

prop.table.tra2_filter.merge <- rbind(prop.table.tra2_filter.cd4, prop.table.trb2_filter.cd4)
  
res.pca.merge.cd4 <- prcomp(t(prop.table.tra2_filter.merge), scale = TRUE, center = T)

mdres.pca.merge.cd4  <-  colnames(prop.table.tra2_filter.merge[,1:14])  %>% 
  as.data.frame() %>% 
  mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp"))

fviz_pca_ind(res.pca.merge.cd4,
             col.ind = as.factor(mdres.pca.merge.cd4$Strain), # color by groups
             legend.title = "Groups",
             geom.ind = "point",
             invisible="quali", pointsize = 0) +
   scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
   geom_point(aes(shape = as.factor(mdres.pca.merge.cd4$Organ),
                  color = as.factor(mdres.pca.merge.cd4$Strain))) + 
  scale_shape_manual(values = c(8,8,8,15,15)) +
  ggtheme() +
  theme(axis.text.x = element_text(angle = 0))

#ggsave("final_fig/pca/cd4.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd4.svg", width = 2.6, height = 1.7)

fviz_pca_ind(res.pca.merge.cd4,
             col.ind = as.factor(mdres.pca.merge.cd4$Strain), # color by groups
             legend.title = "Groups",
             geom.ind = "point",
             invisible="quali", pointsize = 0) +
   scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
   geom_point(aes(shape = as.factor(mdres.pca.merge.cd4$Organ),
                  color = as.factor(mdres.pca.merge.cd4$Strain))) + 
  scale_shape_manual(values = c(8,8,8,15,15)) +
  xlim(c(-30,0)) + ylim(-2,15) +
  ggtheme() +
  theme(axis.text.x = element_text(angle = 0))

#ggsave("final_fig/pca/cd4_zoom.png", width = 2.2, height = 1.4)
#ggsave("final_fig/pca/cd4_zoom.svg", width = 2.2, height = 1.4)

prop.table.tra2_filter.merge <- rbind(prop.table.tra2_filter.cd8, prop.table.trb2_filter.cd8)
  
res.pca.merge.cd8 <- prcomp(t(prop.table.tra2_filter.merge), scale = TRUE, center = T)

mdres.pca.merge.cd8  <-  colnames(prop.table.tra2_filter.merge[,1:14])  %>% 
  as.data.frame() %>% 
  mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
  

fviz_pca_ind(res.pca.merge.cd8,
             col.ind = as.factor(mdres.pca.merge.cd8$Strain), # color by groups
             legend.title = "Groups",
             geom.ind = "point",
             invisible="quali", pointsize = 0) +
   scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
   geom_point(aes(shape = as.factor(mdres.pca.merge.cd8$Organ),
                  color = as.factor(mdres.pca.merge.cd8$Strain))) + 
  scale_shape_manual(values = c(8,8,8,15,15)) +
  ggtheme()

#ggsave("final_fig/pca/cd8.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd8.svg", width = 2.6, height = 1.7)

fviz_pca_ind(res.pca.merge.cd8,
             col.ind = as.factor(mdres.pca.merge.cd8$Strain), # color by groups
             legend.title = "Groups",
             geom.ind = "point",
             invisible="quali", pointsize = 0) +
   scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
   geom_point(aes(shape = as.factor(mdres.pca.merge.cd8$Organ),
                  color = as.factor(mdres.pca.merge.cd8$Strain))) + 
  scale_shape_manual(values = c(8,8,8,15,15)) +
  xlim(c(-32,-18)) + ylim(-4,5) +
  ggtheme()

#ggsave("final_fig/pca/cd8_zoom.png", width = 2.2, height = 1.4)
#ggsave("final_fig/pca/cd8_zoom.svg", width = 2.2, height = 1.4)

## LN
prop.table.tra2_filter.cd4.ln <- prop.table.tra_filter[,1:7]
prop.table.tra2_filter.cd4.ln$sum <- rowSums(prop.table.tra2_filter.cd4.ln)

prop.table.tra2_filter.cd4.ln <- prop.table.tra2_filter.cd4.ln %>% filter(sum >0) %>% select(-sum)

res.pca.tra.cd4.ln <- prcomp(t(prop.table.tra2_filter.cd4.ln), scale = TRUE, center = T)

mdres.pca.tra.cd4.ln  <-  colnames(prop.table.tra_filter[,1:7])  %>% 
  as.data.frame() %>% 
  mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
  

fviz_pca_ind(res.pca.tra.cd4.ln,
             col.ind = as.factor(mdres.pca.tra.cd4.ln$Strain), # color by groups
             legend.title = "Groups",
             geom.ind = "point",
             invisible="quali", pointsize = 0) +
   scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
   geom_point(aes(shape = as.factor(mdres.pca.tra.cd4.ln$Organ),
                  color = as.factor(mdres.pca.tra.cd4.ln$Strain))) +
  geom_hline(yintercept = 0, color = "grey20") +
  geom_vline(xintercept = 0, color = "grey20") 

#ggsave("final_fig/pca/cd4_tra_ln.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd4_tra_ln.svg", width = 2.6, height = 1.7)


## Thymus
prop.table.tra2_filter.cd4.thy <- prop.table.tra_filter[,8:14]
prop.table.tra2_filter.cd4.thy$sum <- rowSums(prop.table.tra2_filter.cd4.thy)

prop.table.tra2_filter.cd4.thy <- prop.table.tra2_filter.cd4.thy %>% filter(sum >0) %>% select(-sum)

res.pca.tra.cd4.thy <- prcomp(t(prop.table.tra2_filter.cd4.thy), scale = TRUE, center = T)

mdres.pca.tra.cd4.thy  <-  colnames(prop.table.tra_filter[,8:14])  %>% 
  as.data.frame() %>% 
  mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
  
fviz_pca_ind(res.pca.tra.cd4.thy,
             col.ind = as.factor(mdres.pca.tra.cd4.thy$Strain), # color by groups
             legend.title = "Groups",
             geom.ind = "point",
             invisible="quali", pointsize = 0) +
   scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
   geom_point(aes(shape = as.factor(mdres.pca.tra.cd4.thy$Organ),
                  color = as.factor(mdres.pca.tra.cd4.thy$Strain))) +
  geom_hline(yintercept = 0, color = "grey20") +
  geom_vline(xintercept = 0, color = "grey20") +
  ggtheme()

#ggsave("final_fig/pca/cd4_tra_thy.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd4_tra_thy.svg", width = 2.6, height = 1.7)

## All
prop.table.tra2_filter.cd8 <- prop.table.tra_filter[,15:28]
prop.table.tra2_filter.cd8$sum <- rowSums(prop.table.tra2_filter.cd8)

prop.table.tra2_filter.cd8 <- prop.table.tra2_filter.cd8 %>% filter(sum >0) %>% select(-sum)

res.pca.tra.cd8 <- prcomp(t(prop.table.tra2_filter.cd8), scale = TRUE, center = T)

mdres.pca.tra.cd8  <-  colnames(prop.table.tra2_filter[,15:28])  %>% 
  as.data.frame() %>% 
  mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp"))

## Error in is.data.frame(x): object 'prop.table.tra2_filter' not found

## LN
prop.table.tra2_filter %>% colnames

## Error in is.data.frame(x): object 'prop.table.tra2_filter' not found

prop.table.tra2_filter.cd8.ln <- prop.table.tra_filter[,15:21]
prop.table.tra2_filter.cd8.ln$sum <- rowSums(prop.table.tra2_filter.cd8.ln)

prop.table.tra2_filter.cd8.ln <- prop.table.tra2_filter.cd8.ln %>% filter(sum >0) %>% select(-sum)

res.pca.tra.cd8.ln <- prcomp(t(prop.table.tra2_filter.cd8.ln), scale = TRUE, center = T)

mdres.pca.tra.cd8.ln  <-  colnames(prop.table.tra_filter[,15:21])  %>% 
  as.data.frame() %>% 
  mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
  
fviz_pca_ind(res.pca.tra.cd8.ln,
             col.ind = as.factor(mdres.pca.tra.cd8.ln$Strain), # color by groups
             legend.title = "Groups",
             geom.ind = "point",
             invisible="quali", pointsize = 0) +
   scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
   geom_point(aes(shape = as.factor(mdres.pca.tra.cd8.ln$Organ),
                  color = as.factor(mdres.pca.tra.cd8.ln$Strain))) +
  geom_hline(yintercept = 0, color = "grey20") +
  geom_vline(xintercept = 0, color = "grey20") 

#ggsave("final_fig/pca/cd8_tra_ln.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd8_tra_ln.svg", width = 2.6, height = 1.7)

## Thymus
prop.table.tra2_filter %>% colnames

## Error in is.data.frame(x): object 'prop.table.tra2_filter' not found

prop.table.tra2_filter.cd8.thy <- prop.table.tra_filter[,22:28]
prop.table.tra2_filter.cd8.thy$sum <- rowSums(prop.table.tra2_filter.cd8.thy)

prop.table.tra2_filter.cd8.thy <- prop.table.tra2_filter.cd8.thy %>% filter(sum >0) %>% select(-sum)

res.pca.tra.cd8.thy <- prcomp(t(prop.table.tra2_filter.cd8.thy), scale = TRUE, center = T)

mdres.pca.tra.cd8.thy  <-  colnames(prop.table.tra_filter[,22:28])  %>% 
  as.data.frame() %>% 
  mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
 
fviz_pca_ind(res.pca.tra.cd8.thy,
             col.ind = as.factor(mdres.pca.tra.cd8.thy$Strain), # color by groups
             legend.title = "Groups",
             geom.ind = "point",
             invisible="quali", pointsize = 0) +
   scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
   geom_point(aes(shape = as.factor(mdres.pca.tra.cd8.thy$Organ),
                  color = as.factor(mdres.pca.tra.cd8.thy$Strain))) +
  geom_hline(yintercept = 0, color = "grey20") +
  geom_vline(xintercept = 0, color = "grey20") 

#ggsave("final_fig/pca/cd8_tra_thy.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd8_tra_thy.svg", width = 2.6, height = 1.7)

res.pca.trb <- prcomp(t(prop.table.trb_filter[,5:32]), scale = TRUE)

## Error in colMeans(x, na.rm = TRUE): 'x' must be numeric

md_trb  <-  colnames(prop.table.trb_filter[,5:32])  %>% 
  as.data.frame() %>% 
  mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp"))

## All
colnames(prop.table.trb2_filter)

## Error in is.data.frame(x): object 'prop.table.trb2_filter' not found

prop.table.trb2_filter.cd4 <- prop.table.trb_filter[,5:18]
prop.table.trb2_filter.cd4$sum <- rowSums(prop.table.trb2_filter.cd4)

prop.table.trb2_filter.cd4 <- prop.table.trb2_filter.cd4 %>% filter(sum >0) %>% select(-sum)

res.pca.trb.cd4 <- prcomp(t(prop.table.trb2_filter.cd4), scale = TRUE, center = T)

mdres.pca.trb.cd4  <-  colnames(prop.table.trb_filter[,5:18])  %>% 
  as.data.frame() %>% 
  mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
 

## LN
prop.table.trb_filter %>% colnames

##  [1] "CD4 Lymph nodes CA Exp01"   "CD4 Lymph nodes CA Exp02"  
##  [3] "CD4 Lymph nodes CA Exp03"   "CD4 Lymph nodes CAKR Exp02"
##  [5] "CD4 Lymph nodes CAKR Exp03" "CD4 Lymph nodes WT Exp02"  
##  [7] "CD4 Lymph nodes WT Exp03"   "CD4 Thymus CA Exp01"       
##  [9] "CD4 Thymus CA Exp03"        "CD4 Thymus CAKR Exp01"     
## [11] "CD4 Thymus CAKR Exp02"      "CD4 Thymus CAKR Exp03"     
## [13] "CD4 Thymus WT Exp02"        "CD4 Thymus WT Exp03"       
## [15] "CD8 Lymph nodes CA Exp02"   "CD8 Lymph nodes CA Exp03"  
## [17] "CD8 Lymph nodes CAKR Exp02" "CD8 Lymph nodes CAKR Exp03"
## [19] "CD8 Lymph nodes WT Exp01"   "CD8 Lymph nodes WT Exp02"  
## [21] "CD8 Lymph nodes WT Exp03"   "CD8 Thymus CA Exp01"       
## [23] "CD8 Thymus CA Exp03"        "CD8 Thymus CAKR Exp02"     
## [25] "CD8 Thymus CAKR Exp03"      "CD8 Thymus WT Exp01"       
## [27] "CD8 Thymus WT Exp02"        "CD8 Thymus WT Exp03"       
## [29] "aaSeqCDR3"                  "allVHitsWithScore"         
## [31] "allDHitsWithScore"          "allJHitsWithScore"

prop.table.trb2_filter.cd4.ln <- prop.table.trb_filter[,5:11]
prop.table.trb2_filter.cd4.ln$sum <- rowSums(prop.table.trb2_filter.cd4.ln)

prop.table.trb2_filter.cd4.ln <- prop.table.trb2_filter.cd4.ln %>% filter(sum >0) %>% select(-sum)

res.pca.trb.cd4.ln <- prcomp(t(prop.table.trb2_filter.cd4.ln), scale = TRUE, center = T)

mdres.pca.trb.cd4.ln  <-  colnames(prop.table.trb_filter[,5:11])  %>% 
  as.data.frame() %>% 
  mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
  

fviz_pca_ind(res.pca.trb.cd4.ln,
             col.ind = as.factor(mdres.pca.trb.cd4.ln$Strain), # color by groups
             legend.title = "Groups",
             geom.ind = "point",
             invisible="quali", pointsize = 0) +
   scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
   geom_point(aes(shape = as.factor(mdres.pca.trb.cd4.ln$Organ),
                  color = as.factor(mdres.pca.trb.cd4.ln$Strain))) +
  geom_hline(yintercept = 0, color = "grey20") +
  geom_vline(xintercept = 0, color = "grey20") 

#ggsave("final_fig/pca/cd4_trb_ln.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd4_trb_ln.svg", width = 2.6, height = 1.7)

## Thymus
prop.table.trb_filter %>% colnames

##  [1] "CD4 Lymph nodes CA Exp01"   "CD4 Lymph nodes CA Exp02"  
##  [3] "CD4 Lymph nodes CA Exp03"   "CD4 Lymph nodes CAKR Exp02"
##  [5] "CD4 Lymph nodes CAKR Exp03" "CD4 Lymph nodes WT Exp02"  
##  [7] "CD4 Lymph nodes WT Exp03"   "CD4 Thymus CA Exp01"       
##  [9] "CD4 Thymus CA Exp03"        "CD4 Thymus CAKR Exp01"     
## [11] "CD4 Thymus CAKR Exp02"      "CD4 Thymus CAKR Exp03"     
## [13] "CD4 Thymus WT Exp02"        "CD4 Thymus WT Exp03"       
## [15] "CD8 Lymph nodes CA Exp02"   "CD8 Lymph nodes CA Exp03"  
## [17] "CD8 Lymph nodes CAKR Exp02" "CD8 Lymph nodes CAKR Exp03"
## [19] "CD8 Lymph nodes WT Exp01"   "CD8 Lymph nodes WT Exp02"  
## [21] "CD8 Lymph nodes WT Exp03"   "CD8 Thymus CA Exp01"       
## [23] "CD8 Thymus CA Exp03"        "CD8 Thymus CAKR Exp02"     
## [25] "CD8 Thymus CAKR Exp03"      "CD8 Thymus WT Exp01"       
## [27] "CD8 Thymus WT Exp02"        "CD8 Thymus WT Exp03"       
## [29] "aaSeqCDR3"                  "allVHitsWithScore"         
## [31] "allDHitsWithScore"          "allJHitsWithScore"

prop.table.trb2_filter.cd4.thy <- prop.table.trb_filter[,12:18]
prop.table.trb2_filter.cd4.thy$sum <- rowSums(prop.table.trb2_filter.cd4.thy)

prop.table.trb2_filter.cd4.thy <- prop.table.trb2_filter.cd4.thy %>% filter(sum >0) %>% select(-sum)

res.pca.trb.cd4.thy <- prcomp(t(prop.table.trb2_filter.cd4.thy), scale = TRUE, center = T)

mdres.pca.trb.cd4.thy  <-  colnames(prop.table.trb_filter[,12:18])  %>% 
  as.data.frame() %>% 
  mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
 
fviz_pca_ind(res.pca.trb.cd4.thy,
             col.ind = as.factor(mdres.pca.trb.cd4.thy$Strain), # color by groups
             legend.title = "Groups",
             geom.ind = "point",
             invisible="quali", pointsize = 0) +
   scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
   geom_point(aes(shape = as.factor(mdres.pca.trb.cd4.thy$Organ),
                  color = as.factor(mdres.pca.trb.cd4.thy$Strain))) +
  geom_hline(yintercept = 0, color = "grey20") +
  geom_vline(xintercept = 0, color = "grey20") 

#ggsave("final_fig/pca/cd4_trb_thy.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd4_trb_thy.svg", width = 2.6, height = 1.7)

## All

prop.table.trb2_filter.cd8 <- prop.table.trb_filter[,19:32]
prop.table.trb2_filter.cd8$sum <- rowSums(prop.table.trb2_filter.cd8)

## Error in rowSums(prop.table.trb2_filter.cd8): 'x' must be numeric

prop.table.trb2_filter.cd8 <- prop.table.trb2_filter.cd8 %>% filter(sum >0) %>% select(-sum)

## Error in `filter()`:
## ! Problem while computing `..1 = sum > 0`.
## Caused by error in `sum > 0`:
## ! comparison (6) is possible only for atomic and list types

res.pca.trb.cd8 <- prcomp(t(prop.table.trb2_filter.cd8), scale = TRUE, center = T)

## Error in colMeans(x, na.rm = TRUE): 'x' must be numeric

mdres.pca.trb.cd8  <-  colnames(prop.table.trb_filter[,19:32])  %>% 
  as.data.frame() %>% 
  mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
  

## LN
prop.table.trb_filter %>% colnames

##  [1] "CD4 Lymph nodes CA Exp01"   "CD4 Lymph nodes CA Exp02"  
##  [3] "CD4 Lymph nodes CA Exp03"   "CD4 Lymph nodes CAKR Exp02"
##  [5] "CD4 Lymph nodes CAKR Exp03" "CD4 Lymph nodes WT Exp02"  
##  [7] "CD4 Lymph nodes WT Exp03"   "CD4 Thymus CA Exp01"       
##  [9] "CD4 Thymus CA Exp03"        "CD4 Thymus CAKR Exp01"     
## [11] "CD4 Thymus CAKR Exp02"      "CD4 Thymus CAKR Exp03"     
## [13] "CD4 Thymus WT Exp02"        "CD4 Thymus WT Exp03"       
## [15] "CD8 Lymph nodes CA Exp02"   "CD8 Lymph nodes CA Exp03"  
## [17] "CD8 Lymph nodes CAKR Exp02" "CD8 Lymph nodes CAKR Exp03"
## [19] "CD8 Lymph nodes WT Exp01"   "CD8 Lymph nodes WT Exp02"  
## [21] "CD8 Lymph nodes WT Exp03"   "CD8 Thymus CA Exp01"       
## [23] "CD8 Thymus CA Exp03"        "CD8 Thymus CAKR Exp02"     
## [25] "CD8 Thymus CAKR Exp03"      "CD8 Thymus WT Exp01"       
## [27] "CD8 Thymus WT Exp02"        "CD8 Thymus WT Exp03"       
## [29] "aaSeqCDR3"                  "allVHitsWithScore"         
## [31] "allDHitsWithScore"          "allJHitsWithScore"

prop.table.trb2_filter.cd8.ln <- prop.table.trb_filter[,19:25]
prop.table.trb2_filter.cd8.ln$sum <- rowSums(prop.table.trb2_filter.cd8.ln)

prop.table.trb2_filter.cd8.ln <- prop.table.trb2_filter.cd8.ln %>% filter(sum >0) %>% select(-sum)

res.pca.trb.cd8.ln <- prcomp(t(prop.table.trb2_filter.cd8.ln), scale = TRUE, center = T)

mdres.pca.trb.cd8.ln  <-  colnames(prop.table.trb_filter[,19:25])  %>% 
  as.data.frame() %>% 
  mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
  
fviz_pca_ind(res.pca.trb.cd8.ln,
             col.ind = as.factor(mdres.pca.trb.cd8.ln$Strain), # color by groups
             legend.title = "Groups",
             geom.ind = "point",
             invisible="quali", pointsize = 0) +
   scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
   geom_point(aes(shape = as.factor(mdres.pca.trb.cd8.ln$Organ),
                  color = as.factor(mdres.pca.trb.cd8.ln$Strain))) +
  geom_hline(yintercept = 0, color = "grey20") +
  geom_vline(xintercept = 0, color = "grey20") 

#ggsave("final_fig/pca/cd8_trb_ln.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd8_trb_ln.svg", width = 2.6, height = 1.7)


## Thymus
prop.table.trb_filter %>% colnames

##  [1] "CD4 Lymph nodes CA Exp01"   "CD4 Lymph nodes CA Exp02"  
##  [3] "CD4 Lymph nodes CA Exp03"   "CD4 Lymph nodes CAKR Exp02"
##  [5] "CD4 Lymph nodes CAKR Exp03" "CD4 Lymph nodes WT Exp02"  
##  [7] "CD4 Lymph nodes WT Exp03"   "CD4 Thymus CA Exp01"       
##  [9] "CD4 Thymus CA Exp03"        "CD4 Thymus CAKR Exp01"     
## [11] "CD4 Thymus CAKR Exp02"      "CD4 Thymus CAKR Exp03"     
## [13] "CD4 Thymus WT Exp02"        "CD4 Thymus WT Exp03"       
## [15] "CD8 Lymph nodes CA Exp02"   "CD8 Lymph nodes CA Exp03"  
## [17] "CD8 Lymph nodes CAKR Exp02" "CD8 Lymph nodes CAKR Exp03"
## [19] "CD8 Lymph nodes WT Exp01"   "CD8 Lymph nodes WT Exp02"  
## [21] "CD8 Lymph nodes WT Exp03"   "CD8 Thymus CA Exp01"       
## [23] "CD8 Thymus CA Exp03"        "CD8 Thymus CAKR Exp02"     
## [25] "CD8 Thymus CAKR Exp03"      "CD8 Thymus WT Exp01"       
## [27] "CD8 Thymus WT Exp02"        "CD8 Thymus WT Exp03"       
## [29] "aaSeqCDR3"                  "allVHitsWithScore"         
## [31] "allDHitsWithScore"          "allJHitsWithScore"

prop.table.trb2_filter.cd8.thy <- prop.table.trb_filter[,26:32]
prop.table.trb2_filter.cd8.thy$sum <- rowSums(prop.table.trb2_filter.cd8.thy)

## Error in rowSums(prop.table.trb2_filter.cd8.thy): 'x' must be numeric

prop.table.trb2_filter.cd8.thy <- prop.table.trb2_filter.cd8.thy %>% filter(sum >0) %>% select(-sum)

## Error in `filter()`:
## ! Problem while computing `..1 = sum > 0`.
## Caused by error in `sum > 0`:
## ! comparison (6) is possible only for atomic and list types

res.pca.trb.cd8.thy <- prcomp(t(prop.table.trb2_filter.cd8.thy), scale = TRUE, center = T)

## Error in colMeans(x, na.rm = TRUE): 'x' must be numeric

mdres.pca.trb.cd8.thy  <-  colnames(prop.table.trb_filter[,26:32])  %>% 
  as.data.frame() %>% 
  mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
 
fviz_pca_ind(res.pca.trb.cd8.thy,
             col.ind = as.factor(mdres.pca.trb.cd8.thy$Strain), # color by groups
             legend.title = "Groups",
             geom.ind = "point",
             invisible="quali", pointsize = 0) +
   scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
   geom_point(aes(shape = as.factor(mdres.pca.trb.cd8.thy$Organ),
                  color = as.factor(mdres.pca.trb.cd8.thy$Strain))) +
  geom_hline(yintercept = 0, color = "grey20") +
  geom_vline(xintercept = 0, color = "grey20") 

## Error in .get_facto_class(X): object 'res.pca.trb.cd8.thy' not found

#ggsave("final_fig/pca/cd8_trb_thy.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd8_trb_thy.svg", width = 2.6, height = 1.7)

Diversity index

md <- read.csv("metadata_Lck.csv")

immdata_tra_wonkt <- repFilter(immdata_tra, "by.clonotype",
  list(V.name = exclude("TRAV11"), J.name = exclude("TRAJ18")),
  .match = "substring")

## ALL TRA
repDiversity(immdata_tra_wonkt$data) %>% vis(.by = c( "Mouse_strain"), .meta = immdata_tra$meta, 
  .signif.label.size = 0) + ggtitle("TRA without NKT") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70")) + ggtitle("all TRA", subtitle = "")

#ggsave("./final_fig/diversity/chao_tra_wonkt_sign.png", width = 8, height = 13, units = "cm")
#ggsave("./final_fig/diversity/chao_tra_wonkt_sign.svg", width = 8, height = 13, units = "cm")


## ALL TRB
repDiversity(immdata_trb$data) %>% vis(.by = c( "Mouse_strain"), .meta = immdata_trb$meta, 
  .signif.label.size = 0) + ggtitle("TRB without NKT") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70")) + ggtitle("all TRB", subtitle = "")

#ggsave("./final_fig/diversity/chao_trb_sign.png", width = 8, height = 13, units = "cm")
#ggsave("./final_fig/diversity/chao_trb_sign.svg", width = 8, height = 13, units = "cm")


## Chao index
repDiversity(immdata_tra_wonkt$data) %>% vis(.by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_tra$meta, .test = F,
  .signif.label.size = 0) + ggtitle("TRA")

#ggsave("./plots/chao_tra.png", width = 15, height = 11, units = "cm")
#ggsave("./plots/chao_tra.svg", width = 15, height = 11, units = "cm")

repDiversity(immdata_trb$data) %>% vis(.by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_trb$meta, .test = F,
  .signif.label.size = 0) + ggtitle("TRB", subtitle = "")

#ggsave("./plots/chao_trb.png", width = 15, height = 11, units = "cm")
#ggsave("./plots/chao_trb.svg", width = 15, height = 11, units = "cm")

chao_tra <- as.data.frame(repDiversity(immdata_tra_wonkt$data))
chao_trb <- as.data.frame(repDiversity(immdata_trb$data))

chao_tra2 <- chao_tra %>% 
  rownames_to_column("num_id") %>% 
mutate(num_id = str_replace_all(string = num_id, c("new_lib_24" = "lib24_S24_L001",  "new_lib_25" = "lib25_S25_L001"))) %>% 
  separate(num_id, into = c("num_id",NA,NA), sep = "_") %>% 
  mutate(num_id = str_replace(num_id, "lib","")) %>% 
  mutate_at(vars("num_id"), as.numeric) 
  

levels_cd4 <- c("CD4 Thymus WT", "CD4 Thymus CA", "CD4 Thymus CAKR", "CD4 Lymph nodes WT", "CD4 Lymph nodes CA", "CD4 Lymph nodes CAKR")

# Attach metadata sent for sequencing

chao_tra2 <- left_join(chao_tra2, md) %>% select(value = Estimator, Cell_type, Organ, Mouse_strain) %>%
   mutate(sample_type = paste(Cell_type, Organ, Mouse_strain)) 

chao_tra2 %>% filter(Cell_type == "CD4") %>% 
  ggplot(aes(y = value, x = factor(sample_type, levels = levels_cd4))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Mouse_strain)) + 
  ylab("Chao index estimate") + 
  ggtitle("CD4 TRA") + 
  theme(axis.text.x = element_text(angle = 90)) + 
  theme_classic() + 
  ggtheme() + 
  theme(axis.text.x = element_text(angle = 90)) +
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")

#ggsave("./final_fig/diversity/chao_cd4_tra.png", width = 8, height = 10, units = "cm")
#ggsave("./final_fig/diversity/chao_cd4_tra.eps", width = 8, height = 10, units = "cm")

levels_cd8 <- c("CD8 Thymus WT", "CD8 Thymus CA", "CD8 Thymus CAKR", "CD8 Lymph nodes WT", "CD8 Lymph nodes CA", "CD8 Lymph nodes CAKR")

chao_tra2 %>% filter(Cell_type == "CD8") %>% 
  ggplot(aes(y = value, x = factor(sample_type, levels = levels_cd8))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Mouse_strain)) + 
  ylab("Chao index estimate") + 
  ggtitle("CD8 TRA") + 
  theme(axis.text.x = element_text(angle = 90)) + 
  theme_classic() + 
  ggtheme() + 
  theme(axis.text.x = element_text(angle = 90)) +
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")

#ggsave("./final_fig/diversity/chao_cd8_tra.png", width = 8, height = 10, units = "cm")
#ggsave("./final_fig/diversity/chao_cd8_tra.eps", width = 8, height = 10, units = "cm")

chao_trb2 <- chao_trb %>% 
  rownames_to_column("num_id") %>% mutate(num_id = str_replace_all(string = num_id, c("new_lib_24" = "lib24_S24_L001",  "new_lib_25" = "lib25_S25_L001"))) %>% 
  separate(num_id, into = c("num_id",NA,NA), sep = "_") %>% 
  mutate(num_id = str_replace(num_id, "lib",""))  %>% 
  mutate_at(vars("num_id"), as.numeric) 
  

levels_cd4 <- c("CD4 Thymus WT", "CD4 Thymus CA", "CD4 Thymus CAKR", "CD4 Lymph nodes WT", "CD4 Lymph nodes CA", "CD4 Lymph nodes CAKR")

chao_trb2 <- left_join(chao_trb2, md) %>% select(value = Estimator, Cell_type, Organ, Mouse_strain) %>%
   mutate(sample_type = paste(Cell_type, Organ, Mouse_strain)) 

chao_trb2 %>% filter(Cell_type == "CD4") %>% 
  ggplot(aes(y = value, x = factor(sample_type, levels = levels_cd4))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Mouse_strain)) + 
  ylab("Chao index estimate") + 
  ggtitle("CD4 TRB") + 
  theme(axis.text.x = element_text(angle = 90)) + 
  theme_classic() + 
  ggtheme() + 
  theme(axis.text.x = element_text(angle = 90)) +
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,125500)) + xlab("")

#ggsave("./final_fig/diversity/chao_cd4_trb.png", width = 8, height = 10, units = "cm")
#ggsave("./final_fig/diversity/chao_cd4_trb.eps", width = 8, height = 10, units = "cm")

levels_cd8 <- c("CD8 Thymus WT", "CD8 Thymus CA", "CD8 Thymus CAKR", "CD8 Lymph nodes WT", "CD8 Lymph nodes CA", "CD8 Lymph nodes CAKR")


chao_trb2 %>% filter(Cell_type == "CD8") %>% 
  ggplot(aes(y = value, x = factor(sample_type, levels = levels_cd8))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Mouse_strain)) + 
  ylab("Chao index estimate") + 
  ggtitle("CD8 TRB") + 
  theme(axis.text.x = element_text(angle = 90)) + 
  theme_classic() + 
  ggtheme() + 
  theme(axis.text.x = element_text(angle = 90)) +
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,125500)) + xlab("")

#ggsave("./final_fig/diversity/chao_cd8_trb.png", width = 8, height = 10, units = "cm")
#ggsave("./final_fig/diversity/chao_cd8_trb.eps", width = 8, height = 10, units = "cm")

chao_trb2 %>% 
  ggplot(aes(y = value, x = factor(Mouse_strain, levels = c("WT","CA","CAKR")))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_dotplot(binaxis='y', stackdir='center', dotsize=0) + 
  geom_jitter(position=position_jitter(0.1), size = 2, aes(color = Mouse_strain)) +
ylab("Chao index estimate") + 
  theme(axis.text.x = element_text(angle = 90)) + 
  theme_classic() + 
  ggtheme() + 
  theme(axis.text.x = element_text(angle = 90)) +
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")

#ggsave("./final_fig/diversity/chao_all_trb.png", width = 8, height = 6, units = "cm")
#ggsave("./final_fig/diversity/chao_all_trb.eps", width = 8, height = 6, units = "cm")


chao_tra2 %>% 
  ggplot(aes(y = value, x = factor(Mouse_strain, levels = c("WT","CA","CAKR")))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_dotplot(binaxis='y', stackdir='center', dotsize=0) + 
  geom_jitter(position=position_jitter(0.1), size = 2, aes(color = Mouse_strain)) +
ylab("Chao index estimate") + 
  theme(axis.text.x = element_text(angle = 90)) + 
  theme_classic() + 
  ggtheme() + 
  theme(axis.text.x = element_text(angle = 90)) +
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")

#ggsave("./final_fig/diversity/chao_all_tra.png", width = 8, height = 6, units = "cm")
#ggsave("./final_fig/diversity/chao_all_tra.eps", width = 8, height = 6, units = "cm")

Top20 WT LN sequences

In the next analyses, we will figure out whether the sequences that are abundant in the periphery of WT animals are also present in LckKO animals. We will select the top 20 most abundant sequences from the lymph nodes of WT animals by calculating the average normalized frequency:

## TRA
topclones <- prop.table.tra_filter %>% 
  mutate(topclones_ln_cd4 = (`CD4 Lymph nodes WT Exp02`+
                               `CD4 Lymph nodes WT Exp03`)/2,
         topclones_ln_cd8 = (`CD8 Lymph nodes WT Exp01`+
                               `CD8 Lymph nodes WT Exp02`+
                               `CD8 Lymph nodes WT Exp03`)/3)

cd4_topclones_20sequences_tra <-
  pull(topclones %>% slice_max(order_by = topclones_ln_cd4, n = 20),
       aaSeqCDR3)
cd8_topclones_20sequences_tra <-
  pull(topclones %>% slice_max(order_by = topclones_ln_cd8, n = 20),
       aaSeqCDR3)

## TRB
topclones <- prop.table.trb_filter %>% 
  mutate(topclones_ln_cd4 = (`CD4 Lymph nodes WT Exp02`+
                               `CD4 Lymph nodes WT Exp03`)/2,
         topclones_ln_cd8 = (`CD8 Lymph nodes WT Exp01`+
                               `CD8 Lymph nodes WT Exp02`+
                               `CD8 Lymph nodes WT Exp03`)/3)

cd4_topclones_20sequences_trb <- pull(topclones %>% 
                                        slice_max(order_by = topclones_ln_cd4, n = 20), aaSeqCDR3)
cd8_topclones_20sequences_trb <- pull(topclones %>% 
                                        slice_max(order_by = topclones_ln_cd8, n = 20), aaSeqCDR3)

Top 20 most abundant TRA sequences in CD4 WT LNs:

cd4_topclones_20sequences_tra

##  [1] "CAASANSGTYQRF"  "CAASASSGSWQLIF" "CAAASSGSWQLIF"  "CAASDYSNNRLTL" 
##  [5] "CAASDTNTGKLTF"  "CAASRNSNNRIFF"  "CAASDQGGRALIF"  "CAASNTNTGKLTF" 
##  [9] "CAASPNTNKVVF"   "CAASSNTNKVVF"   "CAAGTGGYKVVF"   "CAASITGNTGKLIF"
## [13] "CAASDTNAYKVIF"  "CAASSSGSWQLIF"  "CAATGNTGKLIF"   "CAASGGSNAKLTF" 
## [17] "CAASGTGGYKVVF"  "CAASSGSWQLIF"   "CAARSSGSWQLIF"  "CAARNSNNRIFF"

Top 20 most abundant TRB sequences in CD4 WT LNs:

cd4_topclones_20sequences_trb

##  [1] "CASSLGQNTEVFF"  "CASSGTANSDYTF"  "CASSLDSQNTLYF"  "CASSRDNYAEQFF" 
##  [5] "CASSQGQGSYEQYF" "CASSPTGVQDTQYF" "CAWSLGGQDTQYF"  "CASSRDWGYEQYF" 
##  [9] "CASSRDSYAEQFF"  "CASSRDSQNTLYF"  "CASSLGGQNTLYF"  "CASSLGSQNTLYF" 
## [13] "CASSLSQNTLYF"   "CASSLQGNTEVFF"  "CASGDEQYF"      "CASGDAGGQNTLYF"
## [17] "CASSGTANTEVFF"  "CASSQEGGGTEVFF" "CASSRQGNTEVFF"  "CASSPSSYEQYF"

Top 20 most abundant TRA sequences in CD8 WT LNs:

cd8_topclones_20sequences_tra

##  [1] "CAASASSGSWQLIF"  "CALSDRYNQGKLIF"  "CAASNMGYKLTF"    "CAVSASSGSWQLIF" 
##  [5] "CAVDTNAYKVIF"    "CAAASSGSWQLIF"   "CAASSGSWQLIF"    "CAASDNYAQGLTF"  
##  [9] "CAASDTNAYKVIF"   "CAADTNAYKVIF"    "CAVSNMGYKLTF"    "CAASGTGGYKVVF"  
## [13] "CAAGATGGNNKLTF"  "CALMGYKLTF"      "CAASANSGTYQRF"   "CALGDTNAYKVIF"  
## [17] "CAASDDTNAYKVIF"  "CAASDMGYKLTF"    "CAASAGTGGYKVVF"  "CAMREGSSGSWQLIF"

Top 20 most abundant TRB sequences in CD8 WT LNs:

cd8_topclones_20sequences_trb

##  [1] "CASSDAGYEQYF"    "CASSPGTGGYEQYF"  "CASGDAGEQYF"     "CASGDAGGSAETLYF"
##  [5] "CASSDSAETLYF"    "CASSRDNYAEQFF"   "CASSPGQQDTQYF"   "CASSLGYEQYF"    
##  [9] "CASSLGAEQFF"     "CASSPGQYAEQFF"   "CASSLGGNYAEQFF"  "CASSDWGNYAEQFF" 
## [13] "CASSLGANSDYTF"   "CASSPGQNYAEQFF"  "CASSLGGQDTQYF"   "CASGDAGYEQYF"   
## [17] "CASSLGNYAEQFF"   "CASSRDNYEQYF"    "CASSDSYEQYF"     "CASSLDNYAEQFF"

cd4_topclones_rep_pct <- prop.table.tra2 %>% 
  filter(aaSeqCDR3 %in% cd4_topclones_20sequences_tra) %>% 
  select(aaSeqCDR3, starts_with("CD4")) %>% 
  select(1,14,15,9:13,7,8,2:6) 

cd4_topclones_rep_pct2 <- as.data.frame(colSums(cd4_topclones_rep_pct[,2:15]))
colnames(cd4_topclones_rep_pct2) <- "value"

cd4_topclones_rep_pct2 <- cd4_topclones_rep_pct2 %>% 
  rownames_to_column("sample") %>% 
  mutate(sample = stringr::str_replace_all(sample, pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp")) %>% 
  mutate(Cell_type_organ = factor(paste(Organ, Strain), 
                                  levels = c("Thymus WT", "Thymus CA", "Thymus CAKR",
                                             "LN WT", "LN CA", "LN CAKR")))

cd4_topclones_rep_pct2 %>% 
  filter(Organ == "LN") %>% 
  ggplot(aes(y = value*100, x = factor(Cell_type_organ))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Strain)) + 
  ylab("% of repertoire") + 
  ggtitle("CD4 TRA") + 
  theme(axis.text.x = element_text(angle = 90)) + 
  theme_classic() + 
  ggtheme() + 
  theme(axis.text.x = element_text(angle = 90)) +
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")

#ggsave("./final_fig/20topseqs/20topseqs_pct_tra_cd4.png", width = 5, height = 5.7, units = "cm")
#ggsave("./final_fig/20topseqs/20topseqs_pct_tra_cd4.eps", width = 5, height = 5.7, units = "cm")

cd4_topclones_rep_pct <- prop.table.trb2 %>% 
  filter(aaSeqCDR3 %in% cd4_topclones_20sequences_trb) %>% 
  select(aaSeqCDR3, starts_with("CD4")) %>% 
  select(1,14,15,9:13,7,8,2:6) 

cd4_topclones_rep_pct2 <- as.data.frame(colSums(cd4_topclones_rep_pct[,2:15]))
colnames(cd4_topclones_rep_pct2) <- "value"

cd4_topclones_rep_pct2 <- cd4_topclones_rep_pct2 %>% 
  rownames_to_column("sample") %>% 
  mutate(sample = stringr::str_replace_all(sample, pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp")) %>% 
  mutate(Cell_type_organ = factor(paste(Organ, Strain), levels = c("Thymus WT","Thymus CA", "Thymus CAKR",
                                                                   "LN WT","LN CA", "LN CAKR" )))

cd4_topclones_rep_pct2 %>% 
  filter(Organ == "LN") %>% 
  ggplot(aes(y = value*100, x = factor(Cell_type_organ))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Strain)) + 
  ylab("% of repertoire") + 
  ggtitle("CD4 TRB") + 
  theme(axis.text.x = element_text(angle = 90)) + 
  theme_classic() + 
  ggtheme() + 
  theme(axis.text.x = element_text(angle = 90)) +
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")

#ggsave("./final_fig/20topseqs/20topseqs_pct_trb_cd4.png", width = 5.4, height = 5.7, units = "cm")
#ggsave("./final_fig/20topseqs/20topseqs_pct_trb_cd4.eps", width = 5.4, height = 5.7, units = "cm")

cd8_topclones_rep_pct <- prop.table.tra2 %>% 
  filter(aaSeqCDR3 %in% cd8_topclones_20sequences_tra) %>% 
  select(aaSeqCDR3, starts_with("CD8")) %>% 
  select(1,13:15,9:12,6:8,2:5) 

cd8_topclones_rep_pct2 <- as.data.frame(colSums(cd8_topclones_rep_pct[,2:15]))
colnames(cd8_topclones_rep_pct2) <- "value"

cd8_topclones_rep_pct2 <- cd8_topclones_rep_pct2 %>% 
  rownames_to_column("sample") %>% 
  mutate(sample = stringr::str_replace_all(sample, pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp")) %>% 
  mutate(Cell_type_organ = factor(paste(Organ, Strain), levels = c("Thymus WT","Thymus CA", "Thymus CAKR",
                                                                   "LN WT","LN CA", "LN CAKR" )))

cd8_topclones_rep_pct2